import json
from dotenv import load_dotenv
from openai import OpenAI
from pypdf import PdfReader
import gradio as gr


def chat(message, history):
    messages = [{"role": "system", "content": system_prompt}] + \
        history + [{"role": "user", "content": message}]
    done = False
    while not done:
        response = openai.chat.completions.create(
            model="gpt-4o-mini", messages=messages, tools=tools)

        finish_reason = response.choices[0].finish_reason

        if finish_reason == "tool_calls":
            message = response.choices[0].message
            print(message)
            tool_calls_response = fn_handle_tool_calls(message.tool_calls)
            messages.append(message)
            messages.extend(tool_calls_response)
        else:
            done = True

    return response.choices[0].message.content


def fn_handle_tool_calls(tool_calls):
    results = []
    for tool_call in tool_calls:
        tool_name = tool_call.function.name
        arguments = json.loads(tool_call.function.arguments)
        tool = globals().get(tool_name)
        tool_response = tool(**arguments)
        results.append({"role": "tool", "content": json.dumps(
            tool_response), "tool_call_id": tool_call.id})

    return results


def record_user_details(email, name="Name not provided", notes="notes not provided"):
    print(f"""Knock knock! user {name} with email {email} sent this: 
          
          {notes}

          """)
    return {"recorded": "ok"}


def record_unknown_question(question):
    print(f"That's a shame. I couldn't answer this question : {question}")
    return {"recorded": "ok"}


load_dotenv(override=True)
openai = OpenAI()
reader = PdfReader("data/linkedin.pdf")
name = "Vikram Vasudevan"
linkedin = ""
summary = ""
for page in reader.pages:
    text = page.extract_text()
    if text:
        linkedin += text

with open("data/summary.txt", "r", encoding="utf-8") as f:
    summary = f.read()

meta_fn_log_unknown_question = {
    "name": "record_unknown_question",
    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {
                "type": "string",
                "description": "The question that couldn't be answered"
            }
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

meta_fn_log_user_details = {
    "name": "record_user_details",
    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {
                "type": "string",
                "description": "The email address of this user"
            },
            "name": {
                "type": "string",
                "description": "The user's name, if they provided it"
            },
            "notes": {
                "type": "string",
                "description": "Any additional information about the conversation that's worth recording to give context"
            }
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

tools = [{
    "type": "function", "function": meta_fn_log_user_details,
    "type": "function", "function": meta_fn_log_unknown_question
}]

system_prompt = f"You are acting as {name}. You are answering questions on {name}'s website, \
particularly questions related to {name}'s career, background, skills and experience. \
Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \
You are given a summary of {name}'s background and LinkedIn profile which you can use to answer questions. \
Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool \
only if email address is provided and it is NOT blank. \
If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
"

system_prompt += f"\n\n## Summary:\n{summary}\n\n## LinkedIn Profile:\n{linkedin}\n\n"
system_prompt += f"With this context, please chat with the user, always staying in character as {name}."

demo = gr.ChatInterface(chat, type="messages")
demo.launch()